Global Counterfactual Explainer for Graph Neural Networks Article Swipe
Zexi Huang
,
Mert Kosan
,
Sourav Medya
,
Sayan Ranu
,
Ambuj K. Singh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3539597.3570376
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3539597.3570376
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCFExplainer, a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary. Extensive experiments on real graph datasets show that the global explanation from GCFExplainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3539597.3570376
- https://dl.acm.org/doi/pdf/10.1145/3539597.3570376
- OA Status
- gold
- Cited By
- 39
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321485347
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4321485347Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3539597.3570376Digital Object Identifier
- Title
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Global Counterfactual Explainer for Graph Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-22Full publication date if available
- Authors
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Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, Ambuj K. SinghList of authors in order
- Landing page
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https://doi.org/10.1145/3539597.3570376Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3539597.3570376Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3539597.3570376Direct OA link when available
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Counterfactual thinking, Computer science, Popularity, Artificial intelligence, Theoretical computer science, Graph, Machine learning, Artificial neural network, Philosophy, Psychology, Epistemology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
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39Total citation count in OpenAlex
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2025: 13, 2024: 19, 2023: 6, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.limited | 71 |
| abstract_inverted_index.machine | 36 |
| abstract_inverted_index.methods | 64 |
| abstract_inverted_index.minimal | 57 |
| abstract_inverted_index.models. | 38 |
| abstract_inverted_index.natural | 13 |
| abstract_inverted_index.powered | 140 |
| abstract_inverted_index.propose | 135 |
| abstract_inverted_index.through | 110 |
| abstract_inverted_index.various | 7 |
| abstract_inverted_index.Existing | 63 |
| abstract_inverted_index.achieves | 177 |
| abstract_inverted_index.approach | 77 |
| abstract_inverted_index.behavior | 175 |
| abstract_inverted_index.biology, | 12 |
| abstract_inverted_index.compared | 191 |
| abstract_inverted_index.computer | 17 |
| abstract_inverted_index.coverage | 183 |
| abstract_inverted_index.datasets | 160 |
| abstract_inverted_index.explains | 127 |
| abstract_inverted_index.insights | 171 |
| abstract_inverted_index.language | 14 |
| abstract_inverted_index.learning | 37 |
| abstract_inverted_index.networks | 2 |
| abstract_inverted_index.policies | 90 |
| abstract_inverted_index.provides | 168 |
| abstract_inverted_index.recourse | 89, 182, 189 |
| abstract_inverted_index.summary. | 154 |
| abstract_inverted_index.Extensive | 155 |
| abstract_inverted_index.algorithm | 139 |
| abstract_inverted_index.black-box | 35 |
| abstract_inverted_index.cognitive | 94 |
| abstract_inverted_index.important | 169 |
| abstract_inverted_index.objective | 49 |
| abstract_inverted_index.reasoning | 46 |
| abstract_inverted_index.reduction | 187 |
| abstract_inverted_index.security. | 18 |
| abstract_inverted_index.high-level | 170 |
| abstract_inverted_index.increasing | 26 |
| abstract_inverted_index.prediction | 55 |
| abstract_inverted_index.reasoning. | 75, 113 |
| abstract_inverted_index.experiments | 156 |
| abstract_inverted_index.explainers. | 197 |
| abstract_inverted_index.explanation | 67, 165 |
| abstract_inverted_index.limitations | 81 |
| abstract_inverted_index.overloading | 92 |
| abstract_inverted_index.popularity, | 22 |
| abstract_inverted_index.predictions | 31 |
| abstract_inverted_index.processing, | 15 |
| abstract_inverted_index.GCFExplainer | 167 |
| abstract_inverted_index.applications | 5 |
| abstract_inverted_index.information. | 99 |
| abstract_inverted_index.GCFExplainer, | 136 |
| abstract_inverted_index.Specifically, | 114 |
| abstract_inverted_index.computational | 11 |
| abstract_inverted_index.counterfactual | 45, 66, 112, 124, 196 |
| abstract_inverted_index.explainability | 107 |
| abstract_inverted_index.representative | 123 |
| abstract_inverted_index.state-of-the-art | 194 |
| abstract_inverted_index.instance-specific | 73 |
| abstract_inverted_index.vertex-reinforced | 142 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.98006884 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |